Operate and monitor ML/AI models and agentic systems in production. Build AI observability, logging, tracing, and evaluation pipelines. Monitor LLM outputs, detect drift and model degradation, and maintain data/feature pipelines. Develop CI/CD, model versioning, experiment tracking, and automate alerts and incident remediation while collaborating with data scientists and platform teams.
Key Responsibilities AI/ML Model Operations
- Deploy, manage, and monitor machine learning and AI models in production environments.
- Implement model performance monitoring including accuracy, latency, and inference metrics.
- Detect and mitigate concept drift, data drift, and model degradation.
AI Observability
- Design and implement AI observability frameworks to track model behavior and reliability.
- Monitor LLM outputs, hallucination rates, and response quality.
- Implement logging, tracing, and evaluation pipelines for AI systems.
Agentic Systems Monitoring
- Monitor agent-based AI workflows and autonomous systems.
- Track agent actions, tool usage, decision paths, and execution outcomes.
- Implement guardrails, safety monitoring, and failure detection for AI agents.
Data Pipeline Monitoring
- Monitor and maintain data ingestion, transformation, and feature pipelines.
- Ensure data quality, schema consistency, and pipeline reliability.
- Detect and resolve pipeline failures and anomalies.
Infrastructure & Automation
- Build and maintain CI/CD pipelines for ML models and AI systems.
- Manage model versioning, experiment tracking, and reproducibility.
- Automate monitoring alerts, incident response, and remediation.
Collaboration
- Work closely with data scientists, ML engineers, platform teams, and product teams.
- Support continuous improvement of AI system reliability and governance
-
Compensation, Benefits and Duration
Minimum Compensation: USD 50,000
Maximum Compensation: USD 177,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post
Similar Jobs
Aerospace • Artificial Intelligence • Machine Learning • Robotics • Software
Build and maintain governed Silver/Gold data models and semantic assets on Databricks. Translate stakeholder KPIs into testable, reusable transformations, apply enterprise modeling standards, enable domain self-service, ensure data sensitivity and governance, and review domain-contributed models for production readiness.
Top Skills:
Bi ToolsDatabricksDatabricks SqlDelta Live TablesGeniePysparkSemantic Layer ToolingSQL
Aerospace • Artificial Intelligence • Machine Learning • Robotics • Software
Lead enterprise systems and data architecture across business domains, define target-state architectures and integration patterns, rationalize shadow IT, guide sensitive-data placement in regulated multi-environment contexts, support major strategic programs, establish architecture governance and review practices, and coach engineering teams to raise architectural maturity.
Top Skills:
APIsBatch Data MovementDatabricksEvent-Driven ArchitectureOracle ErpSAPSnowflake
Aerospace • Artificial Intelligence • Machine Learning • Robotics • Software
Lead operational reliability and platform enablement for Databricks: build monitoring, CI/CD, deployment standards, compute and job policies, observability, runbooks, and governance to support secure, cost-aware, production data workloads across regulated environments. Mentor engineers and align platform with cloud/infrastructure and compliance requirements.
Top Skills:
Ci/CdDatabricksDatabricks Asset BundlesDatabricks WorkflowsDelta LakeInfrastructure-As-CodeService PrincipalsUnity CatalogVersion Control (Git)
What you need to know about the Seattle Tech Scene
Home to tech titans like Microsoft and Amazon, Seattle punches far above its weight in innovation. But its surrounding mountains, sprinkled with world-famous hiking trails and climbing routes, make the city a destination for outdoorsy types as well. Established as a logging town before shifting to shipbuilding and logistics, the Emerald City is now known for its contributions to aerospace, software, biotech and cloud computing. And its status as a thriving tech ecosystem is attracting out-of-town companies looking to establish new tech and engineering hubs.
Key Facts About Seattle Tech
- Number of Tech Workers: 287,000; 13% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Amazon, Microsoft, Meta, Google
- Key Industries: Artificial intelligence, cloud computing, software, biotechnology, game development
- Funding Landscape: $3.1 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Madrona, Fuse, Tola, Maveron
- Research Centers and Universities: University of Washington, Seattle University, Seattle Pacific University, Allen Institute for Brain Science, Bill & Melinda Gates Foundation, Seattle Children’s Research Institute
